The problem is that I do not know which data frames has the
pass.mobile column. I just list a HDFS directory which contain the
parquet files and some files has the column and some don't. I really
don't want to have conditional logic that inspect the schema. But
maybe that's the only option?
Maybe I misunderstand you, but the following code fails with the same
error as before.
DataFrame dataFrame = ctx.read().parquet(localPath)
.select("pass")
.withColumn("mobile", col("pass.mobile"));
The flatten option works for my use case. But the problem is that
there seems to be no way of dropping nested columns, i.e.
drop("pass.auction")
On Sun, Nov 20, 2016 at 10:55 AM, Mendelson, Assaf
<assaf.mendel...@rsa.com> wrote:
> The issue is that you already have a struct called pass. What you did was add
> a new columned called "pass.mobile" instead of adding the element to pass -
> The schema for pass element is the same as before.
> When you do select pass.mobile, it finds the pass structure and checks for
> mobile in it.
>
> You can do it the other way around: set the name to be: pass_mobile. Add it
> as before with lit(0) for those that dataframes that do not have the mobile
> field and do something like withColumn("pass_mobile", df["pass.modile"]) for
> those that do.
> Another option is to use do something like df.select("pass.*") to flatten the
> pass structure and work on that (then you can do withColumn("mobile",...)
> instead of "pass.mobile") but this would change the schema.
>
>
> -----Original Message-----
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Saturday, November 19, 2016 4:57 PM
> To: Mendelson, Assaf
> Cc: user
> Subject: Re: DataFrame select non-existing column
>
> Thanks. Here's my code example [1] and the printSchema() output [2].
>
> This code still fails with the following message: "No such struct field
> mobile in auction, geo"
>
> By looking at the schema, it seems that pass.mobile did not get nested, which
> is the way it needs to be for my use case. Is nested columns not supported by
> withColumn()?
>
> [1]
>
> DataFrame df = ctx.read().parquet(localPath).withColumn("pass.mobile",
> lit(0L)); dataFrame.printSchema(); dataFrame.select("pass.mobile");
>
> [2]
>
> root
> |-- pass: struct (nullable = true)
> | |-- auction: struct (nullable = true)
> | | |-- id: integer (nullable = true)
> | |-- geo: struct (nullable = true)
> | | |-- postalCode: string (nullable = true)
> |-- pass.mobile: long (nullable = false)
>
> On Sat, Nov 19, 2016 at 7:45 AM, Mendelson, Assaf <assaf.mendel...@rsa.com>
> wrote:
>> In pyspark for example you would do something like:
>>
>> df.withColumn("newColName",pyspark.sql.functions.lit(None))
>>
>> Assaf.
>> -----Original Message-----
>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>> Sent: Friday, November 18, 2016 9:19 PM
>> To: Mendelson, Assaf
>> Cc: user
>> Subject: Re: DataFrame select non-existing column
>>
>> Thanks for your answer. I have been searching the API for doing that but I
>> could not find how to do it?
>>
>> Could you give me a code snippet?
>>
>> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com>
>> wrote:
>>> You can always add the columns to old dataframes giving them null (or some
>>> literal) as a preprocessing.
>>>
>>> -----Original Message-----
>>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>>> Sent: Friday, November 18, 2016 4:32 PM
>>> To: user
>>> Subject: DataFrame select non-existing column
>>>
>>> Hi
>>>
>>> We have evolved a DataFrame by adding a few columns but cannot write select
>>> statements on these columns for older data that doesn't have them since
>>> they fail with a AnalysisException with message "No such struct field".
>>>
>>> We also tried dropping columns but this doesn't work for nested columns.
>>>
>>> Any non-hacky ways to get around this?
>>>
>>> Cheers,
>>> -Kristoffer
>>>
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